96
Views
0
CrossRef citations to date
0
Altmetric
Research Article

High-dimensional control charts with application to surveillance of grease damage in bearings of wind turbines

, , &
Article: 2377739 | Received 09 Oct 2023, Accepted 01 Jul 2024, Published online: 09 Jul 2024

References

  • Abbas, Z., Nazir, H. Z., Abid, M., Akhtar, N., & Riaz, M. (2021). Nonparametric progressive sign chart for monitoring process location based on individual data. Quality Technology & Quantitative Management, 18(2), 225–23. https://doi.org/10.1080/16843703.2020.1827726
  • Ahmad, M. R., & Ahmed, S. E. (2021). On the distribution of the T2 statistic, used in statistical process monitoring, for high-dimensional data. Statistics & Probability Letters, 168, 108919. https://doi.org/10.1016/j.spl.2020.108919
  • Ahmadi-Javid, A., & Ebadi, M. (2021). A two-step method for monitoring normally distributed multi-stream processes in high dimensions. Quality Engineering, 33(1), 143–155. https://doi.org/10.1080/08982112.2020.1786118
  • Amano, R. S. (2017). Review of wind turbine research in 21st century. Journal of Energy Resources Technology, 139(5), 050801. https://doi.org/10.1115/1.4037757
  • Bai, Z., & Saranadasa, H. (1996). Effect of high dimension: By an example of a two sample problem. Statistica Sinica, 6(2), 311–329. https://www.jstor.org/stable/24306018
  • Balakrishnan, N., Triantafyllou, I., & Koutras, M. (2009). Nonparametric control charts based on runs and Wilcoxon-type rank-sum statistics. Journal of Statistical Planning and Inference, 139(9), 3177–3192. https://doi.org/10.1016/j.jspi.2009.02.013
  • Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: An overview. Quality and Reliability Engineering International, 23(5), 517–543. https://doi.org/10.1002/qre.829
  • Bulut, H. (2023). A robust Hotelling test statistic for one sample case in high dimensional data. Communications in Statistics-Theory and Methods, 52(13), 4590–4604. https://doi.org/10.1080/03610926.2021.1996606
  • Chong, Z. L., Mukherjee, A., & Khoo, M. B. (2022). Proposed nonparametric runs rules lepage and synthetic lepage schemes. Computers & Industrial Engineering, 172, 108217. https://doi.org/10.1016/j.cie.2022.108217
  • Dempster, A. P. (1958). A high dimensional two sample significance test. Annals of Mathematical Statistics, 29(4), 995–1010. https://doi.org/10.1214/aoms/1177706437
  • Dempster, A. P. (1960). A significance test for the separation of two highly multivariate small samples. Biometrics Bulletin, 16(1), 41–50. https://doi.org/10.2307/2527954
  • Erem, A., & Mahmood, T. (2023). A bivariate CUSUM control chart based on exceedance statistics. Quality and Reliability Engineering International, 39(4), 1172–1191. https://doi.org/10.1002/qre.3285
  • Fan, J., Shu, L., Yang, A., & Li, Y. (2021). Phase I analysis of high-dimensional covariance matrices based on sparse leading eigenvalues. Journal of Quality Technology, 53(4), 333–346. https://doi.org/10.1080/00224065.2020.1746212
  • Fierro, T. (2016). Extending bearing life in wind turbine mainshafts. Power Engineering, 120(8), 34–40. https://www.power-eng.com/coal/extending-bearing-life-in-wind-turbine-mainshafts/#gref
  • Fujiwara, K., & Kano, M. ( 2017, July 3–6). Development of correlation-based process characteristics visualization method and its application to fault detection. Paper presented at the 2017 13th IEEE International Conference on Control & Automation (ICCA), Ohrid, Macedonia, IEEE. https://doi.org/10.1109/ICCA.2017.8003187
  • He, D., Liu, H., Xu, K., & Cao, M. (2021). Generalized schott type tests for complete independence in high dimensions. Journal of Multivariate Analysis, 183, 104731. https://doi.org/10.1016/j.jmva.2021.104731
  • He, S., Jiang, W., & Deng, H. (2018). A distance-based control chart for monitoring multivariate processes using support vector machines. Annals of Operations Research, 263(1), 191–207. https://doi.org/10.1007/s10479-016-2186-4
  • Hotelling, H. (1947). Multivariate quality control. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of Statistical Analysis (pp. 111–184). New York: McGraw-Hill.
  • Hu, K., & Yuan, J. (2008). Multivariate statistical process control based on multiway locality preserving projections. Journal of Process Control, 18(7–8), 797–807. https://doi.org/10.1016/j.jprocont.2007.11.002
  • Killick, R., Beaulieu, C., Taylor, S., & Hullait, H. (2021). EnvCpt: detection of structural changes in climate and Eenvironment time series (Version R package version 1.1.3) [R package version 1.1.3]: CRAN. https://CRAN.R-project.org/package=EnvCpt
  • Kim, J., Abdella, G. M., Kim, S., Al-Khalifa, K. N., & Hamouda, A. M. (2019). Control charts for variability monitoring in high-dimensional processes. Computers & Industrial Engineering, 130, 309–316. https://doi.org/10.1016/j.cie.2019.02.012
  • Kim, J.-M, Wang, N., Liu, Y., & Park, K. (2020). Residual control chart for binary response with multicollinearity covariates by neural network model. Symmetry, 12(3), 381. https://doi.org/10.3390/sym12030381
  • Ku, W., Storer, R. H., & Georgakis, C. (1995). Disturbance detection and isolation by dynamic principal component analysis. Chemometrics and Intelligent Laboratory Systems, 30(1), 179–196. https://doi.org/10.1016/0169-7439(95)00076-3
  • Lee, J.-M., Yoo, C., Choi, S. W., Vanrolleghem, P. A., & Lee, I.-B. (2004). Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 59(1), 223–234. https://doi.org/10.1016/j.ces.2003.09.012
  • Li, Y., Liu, Y., Zou, C., & Jiang, W. (2014). A self-starting control chart for high-dimensional short-run processes. International Journal of Production Research, 52(2), 445–461. https://doi.org/10.1080/00207543.2013.832001
  • Liu, Z., & Zhang, L. (2020). A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings. Measurement, 149, 107002. https://doi.org/10.1016/j.measurement.2019.107002
  • Maboudou-Tchao, E., Harrison, C. W., & Sen, S. (2023). A comparison study of penalized likelihood via regularization and support vector-based control charts. Quality Technology & Quantitative Management, 20(2), 147–167. https://doi.org/10.1080/16843703.2022.2096198
  • Mahmood, T., & Erem, A. (2023). A bivariate exponentially weighted moving average control chart based on exceedance statistics. Computers & Industrial Engineering, 175, 108910. https://doi.org/10.1016/j.cie.2022.108910
  • Mahmood, T., Nazir, H. Z., Abbas, N., Riaz, M., & Ali, A. (2017). Performance evaluation of joint monitoring control charts. Scientia Iranica, 24(4), 2152–2163. https://doi.org/10.24200/sci.2017.4301
  • Mahmood, T., Wittenberg, P., Zwetsloot, I. M., Wang, H., & Tsui, K. L. (2019). Monitoring data quality issues for telehealth systems in the presence of missing data. International Journal of Medical Informatics, 126, 156–163. https://doi.org/10.1016/j.ijmedinf.2019.03.011
  • Mason, R. L., & Young, J. C. (2002). Multivariate statistical process control with industrial applications. SIAM.
  • Montgomery, D. C. (2020). Introduction to statistical quality control. John Wiley & Sons.
  • Mukherjee, A., & Chakraborti, S. (2012). A distribution‐free control chart for the joint monitoring of location and scale. Quality and Reliability Engineering International, 28(3), 335–352. https://doi.org/10.1002/qre.1249
  • Najarzadeh, D. (2021). Testing independence in high-dimensional multivariate normal data. Communications in Statistics-Theory Methods, 50(14), 3421–3435. https://doi.org/10.1080/03610926.2019.1702699
  • Odom, G. J., Newhart, K. B., Cath, T. Y., & Hering, A. S. (2018). Multistate multivariate statistical process control. Applied Stochastic Models in Business and Industry, 34(6), 880–892. https://doi.org/10.1002/asmb.2333
  • Oyague, F. (2009). Gearbox modeling and load simulation of a baseline 750-kw wind turbine using state-of-the-art simulation codes.
  • Seborg, D. E., Edgar, T. F., Mellichamp, D. A., & Doyle, F. J., III. (2016). Process dynamics and control. John Wiley & Sons.
  • Shaohui, M., Tuerhong, G., Wushouer, M., & Yibulayin, T. (2022). PCA mix-based Hotelling’s T2 multivariate control charts for intrusion detection system. IET Information Security, 16(3), 161–177. https://doi.org/10.1049/ise2.12051
  • Srivastava, M. S., & Du, M. (2008). A test for the mean vector with fewer observations than the dimension. Journal of Multivariate Analysis, 99(3), 386–402. https://doi.org/10.1016/j.jmva.2006.11.002
  • Stoumbos, Z. G., & Sullivan, J. (2002). Robustness to non-normality of the multivariate EWMA control chart. Journal of quality technology, 34(3), 260–276. https://doi.org/10.1080/00224065.2002.11980157
  • Sullivan, J. H., Stoumbos, Z. G., Mason, R. L., & Young, J. C. (2007). Step-down analysis for changes in the covariance matrix and other parameters. Journal of Quality Technology, 39(1), 66–84. https://doi.org/10.1080/00224065.2007.11917674
  • Sun, D., Lu, G., Zhou, H., & Yan, Y. (2013). Condition monitoring of combustion processes through flame imaging and kernel principal component analysis. Combustion Science and Technology, 185(9), 1400–1413. https://doi.org/10.1080/00102202.2013.798316
  • Tracy, N. D., Young, J. C., & Mason, R. L. (1992). Multivariate control charts for individual observations. Journal of Quality Technology, 24(2), 88–95. https://doi.org/10.1080/00224065.1992.12015232
  • Ullah, I., Pawley, M. D. M., Smith, A. N. H., & Jones, B. (2017). Improving the detection of unusual observations in high-dimensional settings. Australian & New Zealand Journal of Statistics, 59(4), 449–462. https://doi.org/10.1111/anzs.12210
  • Yeganeh, A., Johannssen, A., & Chukhrova, N. (2024). The partitioning ensemble control chart for on-line monitoring of high-dimensional image-based quality characteristics. Engineering Applications of Artificial Intelligence, 127, 107282. https://doi.org/10.1016/j.engappai.2023.107282
  • Yeganeh, A., Johannssen, A., Chukhrova, N., Erfanian, M., Azarpazhooh, M. R., & Morovatdar, N. (2023). A monitoring framework for health care processes using generalized additive models and auto-encoders. Artificial Intelligence in Medicine, 146, 102689. https://doi.org/10.1016/j.artmed.2023.102689
  • Yeganeh, A., Johannssen, A., Chukhrova, N., & Rasouli, M. (2024). Monitoring multistage healthcare processes using state space models and a machine learning based framework. Artificial Intelligence in Medicine, 151, 102826. https://doi.org/10.1016/j.artmed.2024.102826
  • Yu, J. (2012). Local and global principal component analysis for process monitoring. Journal of Process Control, 22(7), 1358–1373. https://doi.org/10.1016/j.jprocont.2012.06.008
  • Yu, J., & Liu, X. (2022). One-dimensional residual convolutional auto-encoder for fault detection in complex industrial processes. International Journal of Production Research, 60(18), 5655–5674. https://doi.org/10.1080/00207543.2021.1968061
  • Yucesan, Y., & Viana, F. (2020). Replication Data for: A hybrid modeling for wind turbine main bearing fatigue with uncertainty in grease observations. Annual Conference of the PHM Society, 12(1), 14. https://doi.org/10.7910/DVN/J3IUVR
  • Zou, C., Jiang, W., & Tsung, F. (2011). A LASSO-based diagnostic framework for multivariate statistical process control. Technometrics, 53(3), 297–309. https://doi.org/10.1198/TECH.2011.10034